Assessment of heavy metals contamination in the sediments and mangroves (Avicennia marina) at Yanbu coast, Red Sea, Saudi Arabia

Marine Pollution Bulletin 149 (2019) 110669

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Assessment of heavy metals contamination in the sediments and mangroves (Avicennia marina) at Yanbu coast, Red Sea, Saudi Arabia

T

Omar M.L. Alharbia, Rafat A. Khattaba,b, Imran Alic,d,∗, Yaser S. Binnasera, Adnan Aqeele a

Department of Biology, Faculty of Sciences, Taibah University, Al-Medina Al-Munawara, 41477, Saudi Arabia Department of Marine Science, Faculty of Sciences, Suez Canal University, Ismailia, 41522, Egypt c Department of Chemistry, Faculty of Sciences, Taibah University, Al-Medina Al-Munawara, 41477, Saudi Arabia d Department of Chemistry, Jamia Millia Islamia, New Delhi, India e Department of Geology, Faculty of Sciences, Taibah University, Al-Medina Al-Munawara, 41477, Saudi Arabia b

A R T I C LE I N FO

A B S T R A C T

Keywords: Heavy metals pollution Sediments and mangroves (Avicenia marina) Marine pollution National and international comparisons Yanbu red sea

The objectives of this study are to assess the spatial distribution and the bio-accumulation of heavy metals (Cr, Cu, Ni, Pb, and Zn) in the marine sediments and the mangroves (Avicennia marina) at Yanbu Red Sea, Saudi Arabia. Cr, Cu, Ni, Pb and Zn concentrations in the sediments were 14.9–289, 17.2–217.2, 27.3–241.8, 11.5–111.3 and 48.8–511.5 μg g−1, respectively. The values in the roots were 16.3–40.5, 16.8–37.3, 17.2–38.2, 2.4–6.7 and 31.5–62.4 μg g−1 while the concentrations in the leaves were 14.2–50.1, 18.1–40.2, 16.1–56.3, 2.3–9.9 and 36.8–84.9 μg g−1, respectively. The pollution load was estimated in terms of the different ecological pollution indices such as geo-accumulation, contamination factors and pollution index, potential ecological risk, potential toxicity response indices and biological concentration factors. Briefly, the coastal area of the Red Sea at Yanbu is contaminated with heavy metals, which may affect the quality of the aquatic lives and human beings.

1. Introduction The heavy metals contamination is a worldwide problem in water, soils, plants and even to our foodstuffs (Alharbi et al., 2018a; Ali et al., 2005, 2017; 2018; Wang et al., 2018). This is due to increasing industrial development, domestic activities and overgrowth of the population. Some metals are essential to human beings; particularly copper and zinc while others are toxic such as mercury, lead, cadmium, etc. These are contaminating our ecology including marine ecosystems (Gu et al., 2018; Hao et al., 2019). Generally, the metal ions are very toxic, carcinogenic and non-biodegradable (Feng et al., 2017); with long persistence in the environment (Ali et al., 2005; Feng et al., 2017). The coastline of the Red Sea at Yanbu is very important in Saudi Arabia for providing sea-foods and marine transportation facilities, and consequently, associated with the public health directly or indirectly. Therefore, it is important to study the metal ions hazard in the coastal region of the Red Sea at Yanbu, Saudi Arabia. Generally, the metal ions contamination of the marine system is evaluated by analyzing the metal ions in the sediments and mangroves because these serve as the potential indicators and accumulators of toxic metals ions. The marine sediments play a critical role in the metal ions accumulation and, hence, responsible for metal ions sink; exceeding the

∗

concentrations in water (Mashiatullah et al., 2015). The marine sediments are recommended as the best indicators for tracking the spatial and temporal accumulation of metal ions. It is due to the facts that the marine sediments historically record the ecological events and allow the expectation of future changes (Birch, 2017). Besides, the quality of interstitial and overlying water is intensely affected by the physical (remobilization), chemical (adsorption and desorption) and biological (bioabsorption) properties of the sediments (Chakraborty et al., 2015). Most of the metal ions; entering into the marine environment; are adsorbed onto the suspended solids and organic carbon contents, and finally precipitated and settled down onto the underlying sediments. The marine sediments act as traps for metal ions and mostly perform as exceptional indicators of human activities (Chakraborty et al., 2015). Although the marine sediments can retain heavy metal ions yet cannot keep them permanently because of their disintegration and re-discharge into water. Therefore, marine sediments play an important role in the storage and transport of dangerous metal ions within the marine ecosystem (Jahan and Strezov, 2018). The mangroves are distributed along the inter-tidal wetlands and situated in temperate, tropics, and sub-tropics coastal systems. These are major producers and highly useful in coastal estuarine ecosystems. The mangroves ecosystems serve as nursery and habitat area for many

Corresponding author. Department of Chemistry, Faculty of Sciences, Taibah University, Al-Medina Al-Munawara, 41477, Saudi Arabia. E-mail addresses: [email protected], [email protected] (I. Ali).

https://doi.org/10.1016/j.marpolbul.2019.110669 Received 18 April 2019; Received in revised form 12 October 2019; Accepted 14 October 2019 Available online 22 October 2019 0025-326X/ © 2019 Published by Elsevier Ltd.

Marine Pollution Bulletin 149 (2019) 110669

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shellfish and fish juveniles, which provide direct and indirect social and economic value (Alongi, 2002). These also stabilize and mitigate coastal landforms against erosion (Harty, 1997; MacFarlane et al., 2007). Generally, the mangroves play a significant role in stabilizing and protecting coastal zones from tidal events and erosion and keeping off damaging consequences of natural disasters such as tsunamis and hurricanes. Besides, the mangroves host about 4100 species of fauna and flora; accounting for the global economic rate of 181 billion dollars (Fernandez et al., 2014). The mangroves are exposed to a variety of anthropogenic agents and contaminants. The wastewater effluents, industrial discharges, marine activities are the major causes of the mangroves contamination. Their importance and enlarged population density in shoreline areas have led to the conversion of many mangroves areas to other uses such as unsustainable forestry and land reclamation (West et al., 1983; Eong, 1995). Due to their close vicinity to urban growth, mangroves plants have received substantial direct anthropogenic input of heavy metal ions contamination (MacFarlane, 2002). The metal ions contamination of the mangroves habitats arises due to agricultural runoff, industrial and urban effluents, chemical spills, boating, sewage treatment plants, mining operations and leaching from domestic garbage (Peters et al., 1997). The heavy metal ions contamination in the mangroves environment is getting increased attention over the past few decades (Bodin et al., 2013; Fernandes et al., 2012). High heavy metal ions concentrations in the sediments in the mangroves swamps areas were found damaging to the mangroves (Analuddin et al., 2017; Chai et al., 2019; Feng et al., 2017; Fernandes et al., 2012; Kulkarni et al., 2018; Thanh-Nho et al., 2019). It is because the sediments near the mangroves have FeS2, FeS, free H2S and sulfur, which aid to trap metal ions in the sediment. Thus, the sediments in the mangrove areas may act as sinks for heavy metal ions by sequestering allochthonous organic matter from terrigenous sources. In this way, the contamination of mangroves is a serious issue. Kulkarni et al. (2018) presented a review of the metal ions contamination in the mangroves. The authors reported the presence of some metal ions in the mangroves located in the different parts of the world. In spite of great ecological significance of the mangroves, these are rapidly disappearing (50% lost over the past 50 years) due to the direct anthropogenic interferences (Bouillon, 2011). Globally, the mangroves ecosystems are considered as the most endangered natural systems and are often affected by anthropogenic activities such as urban and agricultural runoff, solid waste disposal, and effluent discharge (Bastakoti et al., 2018). As a result of low soil fertility, poor quality and high salinity, the mangroves at the Red Sea are exposed to stress and, consequently, not capable to grow in a good habitat (Mandura et al., 1987, 1988). There are some papers describing metal ions in the Red Sea at Yanbu (Abohassan, 2013; Al Harbi et al., 2018b; Badr et al., 2009; AlZaharani et al., 2018) but these studies are not complete. Moreover, there is no information on metal ions contamination in the mangroves. The objectives of this study are to use a wide range of environmental quality indices such as the geo-accumulation index, pollution load index, potential ecological risk index, sediment pollution index, factor analysis and statistical analysis to assess the metal ions contamination in the sediment and mangroves in the Red Sea at Yanbu, Saudi Arabia. This is a tool for stakeholders and comparative authorities to manage and protect the biota and aquatic ecosystems. In view of the above facts, the metal ions concentrations were determined in the sediments, leaves and roots of the mangrove plants. In this study, the ability of the mangroves to transport and accumulate metal ions in their tissues is also determined. This is the first and extensive study of the metal ions contamination of the sediments and mangroves plants of the coastal area of Yanbu, Saudi Arabia.

Fig. 1. The mangroves vegetation at Yanbu Red Sea, Saudi Arabia. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

2. Experimental 2.1. Study area Yanbu is considered one of the most active cities in terms of the development and accomplishments along the Saudi coast of the Red Sea. The existence of mangroves (Avicennia marina) and nearby environment at Yanbu area are significantly important. These are fringing the shoreline of Yanbu and distributed among separated populations due to the desert climatic conditions and elevation of Red Sea salinity. Due to the absence of the rivers and estuaries, the mangroves at Yanbu have a shrubby habit and dwarf height (Fig. 1). The coast of Yanbu is extended from Sharm Yanbu (north) to Industrial Yanbu (south); covering a linear distance of ∼35 km. The coastline comprises a widespread shallow littoral marine environment and a complex offshore barrier reef system. The shoreline involves sedimentary sand planes (Fig. 2) and low lying hills that are extending for more than 10 km inland; with salt wetlands found at various locations in the intertidal area. The industrialized city of Yanbu occupies an area of 158 km2 and extends to ∼15 km of the shoreline. The city holds the biggest oil shipping complex in the Red Sea as well as many mineral and petrochemical services. The sampling locations are shown in Fig. 2. Fourteen different surficial (1–10 cm) sediment samples (available in the approach) were collected along the coastal line of the study area (Fig. 2). The sample locations were selected based on the accessibility either by four-wheel-drive car or walking to cover the whole coastal zone of the study area of interest (Fig. 2). The sample locations were determined using Garmin Global Positioning System (GPS) as shown in Fig. 2. The samples were put in plastic bags and then transported to a geotechnical lab to perform mechanical soil grain size analysis as well as soil classification based on the Wentworth scale. Total fourteen samples (sites 1 to 14) of the sediments have been collected while the eight samples of mangroves (4 leaves and 4 aerial roots) were collected at only four available sites (1, 11, 13 and 14).

2.2. Geology of the study area Saudi Arabia is located on the South-Western part of the Arabian Plate and may geologically be divided into four distinct terrains (Fig. 3) (Al-Dabbagh, 2014; Aqeel, 2016). These terrains range from oldest to youngest are - i) The Precambrian Proterozoic Arabian Shield, ii) the Phanerozoic Arabian Continental Platform or Shelf, iii) the Tertiary and Quaternary lava fields or Harrats (extensive basalt plateaus); mainly overlay the shield; and iv) the Tertiary-Quaternary sedimentary rocks and coral reefs; occupy narrow Red Sea coastal plain (Camp ad Roobol, 2

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Fig. 2. The study area along with sampling sites.

and permeability as compared to the other adjacent Harrats terrain (Aqeel, 2016). Accordingly, those sediments have many more capabilities to get contaminated the pollutants.

1991; Aqeel, 2016). The study area of this research is a part of the Red Sea coastal plain sediments (Fig. 3). The sediments are unconsolidated earthen materials and belong to the quaternary period overlying tertiary-quaternary volcanic rocks (Harrats). These are called as soils in geoengineering literature. Geologically, the sediments (soils) are unconsolidated and lose materials that are characterized by high porosity 3

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Fig. 3. General geology of the study area (retrieved after Johnson, 1998).

Aqeel et al., 2017). Accordingly, this method was used as well in this research.

2.3. Instruments used The instrument used was Inductively Coupled Plasma–Mass Spectrometry (ICP-MS) (model 7500) of Agilent, USA. External calibration was applied to calibrate the ICP-MS analysis. Cr, Cu, Ni, Pb and Zn metal ions calibration curves were achieved using the blank and three working standards i.e. 0.5, 10, 50, 100, and 200 μg/L. It is started from a 100 mg/L multi-element standard solution for ICP-MS (Panreac, 766333. 1208). All the elements had exhibited good linearity in their calibration curves.

2.5. Digestion of samples The collected sediments and mangrove samples were digested as per the standard procedures (Alharbi et al., 2018b; El-Sorogy et al., 2018). The sediments were smoothed once dried using mortar and pistol, then sieved with 63 μm sieve. 0.2 g of all fine (< 63 μm) sediments samples were digested separately in a clean Teflon beaker. The digestion was achieved by adding 2.0 mL of HNO3, 6.0 mL of HCl and 2.0 mL of HF to Teflon beaker. The sediments were digested on the hot plate at 120–150 °C for 50 min. The leaves and aerial root parts of mangroves were washed primarily by deionized water in the laboratory and airdried, then, 0.2 g leaves and roots samples were digested using concentrated nitric acid (HNO3), separately. The solubilized metal ions were filtered and relocated to a volumetric flask and adjusted to 50 mL volume with deionized water. A blank digest was carried out under similar conditions. Trace metal ions were determined by Inductively

2.4. Characterization of sediment samples 2D Geospatial distribution maps of heavy metals concentrations i.e. Pb, Zn, Cr, Ni, and Cu have been detected in the study area. These were produced using Arc-GIS-version 10.2 software (ESRI Cor. Inc.). Inverse Distance Weighted (IDW) is a commonly utilized GIS-interpolation method for water and soil pollution (Singh et al., 2011; Balakrishnan et al., 2011; Igboekwe and Akankpo, 2011; Krishnaraj et al., 2015; 4

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Coupled Plasma-Mass Spectrometry; Agilent ICP-MS model 7500. All the experiments were analyzed three times (n = 3).

Table 2 Classification criteria of ecological risk index (E i ) and toxicity response index r (RI) based on Håkanson [1980].

To assess the environmental pollution, the geochemical, physical and chemical features of the sediments are standard enough to identify the sources as well as the intensity of the contamination. Therefore, the geo-accumulation index (Igeo) is used to determine the progressive variation of heavy metals by comparing the present-day metal concentrations with the geochemical background (pre-civilized background values). Geo-accumulation index; introduced by Müller (1979); has been applied in most of the recent pollution studies for the contamination of the sediments by heavy metals, which is calculated by the following equation:

Cn ⎤ Igeo = log 2 ⎡ ⎥ ⎢ 1.5 . Bn ⎦ ⎣

Toxicity (RI)

Potential (Eri )

2.6. Geo-accumulation index (Igeo) calculation

Ecological risk level

Eri < 40

RI < 150

Low risk (LR)

40 ≤ Eri < 80

150 ≤ RI < 300

Moderate risk (MR)

80 ≤ Eri < 160

300 ≤ RI < 600

Considerable risk (CR)

160 ≤ Eri < 320

–

High-risk (HR)

Eri ≤ 320

600 ≤ RI

Very high risk (VHR)

2.8. Calculation of potential ecological risk indices (Eri ) and potential toxicity response indices (RI) The potential ecological risk coefficient (Eri ) was estimated using the formula given by Håkanson (1980), which is as follows:

Eri = Tri ∗ Cri = Tri ∗ Csi/ Cni

(1)

where,

where Cn is the concentration of metal ions, n is measured in soil. Bn is the geochemical background value in the upper continental earth's crust (Turekian and Wedepohl, 1961). The constant 1.5 was used to account for potential variability in the reference value due to the influence of lithogenic processes. In this regard, seven classes of Igeo were categorized by Müller (1981) as shown in Table 1.

Tri is

(4)

metal ions toxic response factor (Cr = 2, Cu = 5, Ni = 5,

Pb = 5 and Cd = 30), C i is the contamination factor, C i is the conr s centration of heavy metal ions in the sediment and C i is a background n value for heavy metal ions. The degree of E i can be categorized as r shown in Table 2. The potential toxicity response index (RI) is a method for calculating the sum of different risk factors. It is commonly used to evaluate the toxicity of various heavy metals in the soil. Håkanson (1980) described RI as the index that determines the heavy metal toxicity and the subsequent environmental response to all five risk factors (Pb, Cd, Cu, Zn, and Cr) in the soils. The potential ecological risk index (RI) was calculated as follow:

2.7. Contamination factor and pollution index calculation The contamination factor (CF) is a major tool for identifying contamination levels in the environmental matrix. It is considered as the ratio of the concentration of each metal in the sediment divided by the background value. It is given by the following formula:

RI =

∑ Eri

(5)

The classification criteria for RI classes are presented in Table 2. (2)

CF = Ci ⁄Bi

2.9. 9. biological concentration factor (BCF)

where Ci is metal ion concentration in soil and Bi is the background value, which refers to the concentration of metal ions in the soils; when there is no anthropogenic input (Taylor and McLennan, 1995). According to Håkanson (1980), criteria applied were - CF < 1 indicates low contamination; 1 < CF < 3 is moderate contamination; 3 < CF < 6 is considerable contamination and CF > 6 is very high contamination. The pollution load index (PLI) is the estimated geometric mean of the relative concentration factors of the selected heavy metal ions of a seemingly polluted site. According to Tomlinson et al. (1980), PLI is considered as a combined tool and used to assess the amount of pollution at a site for a particular set of heavy metals. The PLI value equal to 1 describes the potential absence of the pollution, whereas PLI > 1 indicates the polluted nature of the site. The following equation is used for calculating the PLI:

PLI = (CF1 x CF2 x CF3 x………..x CFn)1/ n

To find out the distribution and the level of heavy metals uptake, the bio-concentration factor is assessed. BCF is a ratio of metal concentration in specified tissues (roots, shoot and leaves) to the concentration in the adjacent environment (Mackay and Fraser, 2000). The bio-concentration factor is calculated by the following equations:

Igeo ≤ 0 0 < Igeo < 1 1 < Igeo < 2 2 < Igeo < 3 3 < Igeo < 4 4 < Igeo < 5 5 < Igeo

0 1 2 3 4 5 6

Uncontaminated (UC) Uncontaminated to Moderately contaminated (UMC) Moderately contaminated (MC) Moderately to heavily contaminated (MHC) Heavily contaminated (HC) Heavily to extremely contaminated (HEC) Extremely contaminated (EC)

sediment

(6)

=C

leaf

/C

sediment

(7)

leaf

2.10. Translocation factor (TF) Translocation is a plant capability to absorb and distribute metal ions across the body. Both translocation factors (TF) and bioaccumulation factors (BAF) were used as potential indicators to estimate the plant phytoremediation (Yoon et al., 2006). The translocation factors (TF) for studied heavy metals are calculated by the following equation.

Table 1 Geo-accumulation index (Igeo) classes as classified by Müller [1981]. Sediment quality

/C

where, Croot, C leaf and Csediment are the heavy metals concentrations in the root, leaves and the sediment sample, respectively.

where, n = number of metals and CF = contamination factor.

Igeo class

root

BCF

(3)

Igeo value

BCFroot = C

TF

leaf

= Cleaf / Croot

(8)

where, Croot, C leaf are the heavy metal concentrations in the root and leaves, respectively (Usman et al., 2012). 3. Results and discussion The contamination levels were discussed using the different pollution indices viz. geo-accumulation index (Igeo), contamination factor 5

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maximum, average and average shale values of the reported metal ions are also given in Table 5 for the comparison purpose. These values ranged from 14.9-48.8, 289.0–511.5, 86.7–144.7 and 90–95 μg g-1; indicating moderate to high pollution. The standard deviations of all the analyses were in the range of 0.962–1.439. The pollution trend was also drawn in the form of maps. The chromium metal ion (the most toxic) is shown in Fig. 6, while the other maps are given in the supplementary information. Generally, the pollution concentration maps showed that the zones at stations 10 and 12 had almost the highest concentrations of the heavy metals. It is again due to the vicinity of points 10 and 12 with the industrial area as well as the muddy and fine nature of the sediments at these two points. In contrast, the zones at stations 1, 4, 13 and 14 had the lowest concentrations of heavy metals. It is well-known that the porosity of the soil (percent of voids) becomes higher with decrease grain sizes of the soil; leading to the accommodation of more polluted liquids. As a result, fine sand soil covering the major part of the study area is prone to high contamination as compared to the other types of soil in the study area.

Table 3 The percentage of the grain size of the sediments (soil) in the study area. Grain Size Category

Wentworth Grain Size Scale (mm)

Percentage of the gain size Min%

Gravel Sand Very coarse sand Coarse sand Medium sand Fine sand Very fine sand Mud (Clay/Silt)

Max%

> 2 up to 4 1–2

0 1.96

0.5–1 0.25–0.5 0.125–0.25 0.063–0.125

3.04 1.11 1.58 0.79

80.82 14.44 56.68 31.99

< 0.063

0.07

64.32

16.46

83.07 36.23

99.41

(CF), pollution load index (PLI) and potential toxicity response index (RI), bioaccumulation factor (BCF) and translocation factor (TF). 3.1. Characterization of the sediment samples

3.3. Assessment of the sediment heavy metal pollution levels Based on the mechanical analyses of the collected surficial sediments, the study area has a broad distribution of grain size. Three different main categories of the soil particles were identified in the study area i.e. i) sand particles with a grain size percentages ranging from 16.46 to 99.41; ii) gravel particles with grain size percentage ranging from 0.0 to 83.07 and iii) mud particles with a maximum percentage of grain size of 64.32 (Table 3). According to the Wentworth's grain size classification, the majority of the grain sizes were in the range of fine sandy soil (0.125–0.25 mm) and coarse sandy soil (0.5–1 mm) as given in Tables 3 and 4). Only the sediments of the two stations (St. 11 and St.13) were classified as gravelly soil. Besides, sediment at St.10 was classified as mud soil. The authors believed that the construction process of the man-made creek in the past was resulted in producing an abundant amount of mud-size particles from which sediment of station 10 was collected (Fig. 2, Table 4). The producing map of these soil types; covering the surface of the study area; did not only simplify 2D geospatial distribution of these soils (Fig. 4) but also might help linking among the pollutants concentrations and the soil types in the study, which is explained later on in this article.

3.3.1. Geo-accumulation index (Igeo) The geo-accumulation index (Igeo) is widely used and effective in explaining the quality of the sediments (Praveena et al., 2008). The obtained values of Igeo are given in Table 6, which were compared with the data of Table 1. After comparison, it was observed that the site 10 was moderately polluted with chromium, copper, lead and zinc along with heavily contaminated by nickel-metal ion (heavy contaminated). Besides, Igeo for nickel was high at points 2 (1.29) and 12 (2.48); indicating moderate contamination. Additionally, lead and zinc had 1.18 and 1.46 as Igeo values at site 12; indicating moderate metal ions pollution. 3.3.2. Contamination factor and pollution load index The contamination factor (CF) is the main feature to determine the contamination and the pollution stages in the environmental medium. The values of CF were calculated as per equation (2) and recorded in Table 7. Besides, the pollution load index (PLI) is also supposed as a combined way to assess the extent of the pollution at any point. PLI value equal to 1.0 defines the absence of contamination, while PLI > 1 shows the contamination of the site. PLI values were calculated as per equation (3) and given in Table 7. A perusal of Table 7 indicates that the values of CF for Cr, Cu, Ni, Pb, and Zn were varied in the range of 0.17–1.50, 0.38–4.83, 0.40–3.56, 0.58–2.29 and 0.51–3.38, respectively. The minimum, maximum and average values were in the range of 0.17–0.51, 3.21–5.38 and 0.96–1.52; indicating moderate pollution. The range of the standard deviation (0.962–1.439) confirmed the validity of the results. Also, an evaluation of Table 7 dictates that the PLI values were found to be high (PLI > 1) in six of the studied sites; indicating moderate pollution and subsequent deterioration of the sediment quality (Banerjee et al., 2016). The PLI values in the present study ranged from 0.41 to 4.40. The obtained higher PLI value might be directly associated with higher concentrations of heavy metals in the sites; particularly at sites 10 (PLI = 4.40) and 12 (PLI = 3.08). Briefly, the sites 1, 2, 7, 10, 12 and 14 are contaminated with high pollution load at sites 10 and 12.

3.2. Heavy metal concentrations in the sediments The various metal ions detected in the sediment samples were Cr, Cu, Ni, Pb, and Zn. The concentrations of these analyzed metal ions were in the ranges of 14.9–289, 17.2–217.2, 27.3–241.8, 11.5–111.3 and 48.8–511.5 μg g−1, respectively. The details of the concentrations of the metal ions are given in Table 5. The spatial distributions of the concentrations of the analyzed metal ions with significant inter-sites variability is shown in Fig. 5. The metal ions concentrations were in the order of Zn > Cr > Ni > Cu > Pb. The localized accumulation of metal ions (Cr, Cu, Ni, Pb and Zn) have been particularly observed at sites 10 and 12. This is due to the fact that the points 10 and 12 are located near the industrial area of Yanbu city. The minimum, Table 4 The soil classification and distribution of the collected surficial sediments in the study. area. Soil Types Coarse soil

Fine soil

3.3.3. Potential ecological risk indices (Eri ) and potential toxicity response indices (RI) The potential ecological risk coefficients (Eri ) and potential toxicity response indices (RI) are used to determine the toxicity. The values of these factors were estimated using equations (4) and (5) and recorded in Table 8. The values of ecological risk coefficients for Cr, Cu, Ni, Pb, and zinc were in the range of 0.33–6.42, 1.91 to 24.13, 2.40 to 21.34, 2.89 to 27.83, and 0.51 to 5.38, respectively. The values of potential toxicity response indices were in the range of 8.22–85.10. The values of

Distributions Gravelly soil Sandy soil

Very coarse Coarse Medium Fine Very fine Mud (Clayey/Silty)

St. 11 & St. 13 St.4 St.3, St.7, & St.9, & St.14 None St.1, St.2, St. 5, St.6, St.8, & St.12 None St. 10

6

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Fig. 4. The surficial soil types and distribution in the study area.

3.4. Heavy metal concentrations in the Mangroves

Table 5 Spatial variation of analyzed metals concentrations (μg g-1) in surface sediments collected from Yanbu shorelines. Sites

1 2 3 4 5 6 7 8 9 10 11 12 13 14 Min. Max. Average Average shale

The different concentrations of these metal ions in the roots and leaves of mangroves are given in Table 9. An evaluation of this Table indicates 16.3 to 40.5, 16.8 to 37.3, 17.2 to 38.2, 2.4 to 6.7 and 31.5–62.4 μg g−1 as ranges of Cr, Cu, Ni, Pb and Zn metal ions concentrations. These ranges in case of leaves were 14.2–50.1, 18.1 to 40.2, 16.1 to 56.3, 2.3 to 9.9 and 36.8–84.9 μg g−1, respectively. The average values for these metal ions in the roots and leaves were 27.0, 23.1, 27.9, 3.6 & 43.3 and 27.9, 29.0, 32.5, 4.8 & 59,8 μg g−1. The minimum values for these metal ions in both roots and leaves were 16.3, 16.8, 17.2, 2.4 & 31.5 and 14.2, 18.1, 16.1, 2.3 & 36.8 μg g−1, respectively. On the other hand, the maximum values in the roots and leaves were 40.5, 37.3, 38.2, 6.7 & 62.4 and 50.1, 40.2, 56.3, 9.9 & 84.9 μg g−1. These values indicate quite high metal ions contaminations in the mangroves. Generally, the concentrations of these metal ions were higher in roots than leaves. It is because of the fact that the roots are in direct contact with the previously contaminated sediments due to these metal ions.

Heavy metals concentrations (μg g-1) Cr

Cu

Ni

Pb

Zn

14.9 134.8 83.6 35.3 55.2 31.3 99.7 22.9 31.2 289.0 47.2 260.9 31.4 77.0 14.9 289.0 86.7 90

20.3 37.7 48.7 42.4 18.7 17.3 32.9 17.2 17.3 217.2 28.0 123.7 20.4 32.3 17.2 217.2 48.1 45

42.2 73.5 49.0 28.2 39.5 34.5 49.2 27.3 29.4 241.8 46.3 167.7 33.7 43.9 27.3 241.8 64.7 68

12.9 39.4 39.8 45.8 17.7 13.5 35.5 11.5 29.6 111.3 15.4 68.1 44.4 45.7 11.5 111.3 37.9 20

64.1 169.5 115.3 108.8 89.1 51.2 106.5 48.8 65.7 511.5 75.4 391.9 78.7 150.0 48.8 511.5 144.7 95

3.4.1. Biological concentration factor (BCF) Generally, the distribution of the metal ions in the plant's parts is calculated by the bio-accumulation factor. To find out the distribution as well as the levels of heavy metals uptake, the bio-concentration factor was assessed. The values of BCF were calculated as per equations (6) and (7) and are given in Table 10. The values of BCF in leaves; for Cr, Cu, Ni, Pb and Zn; were in the range of 0.05–3.36, 0.15 to 1.98, 0.10 to 1.33, 0.03 to 0.77 and 0.09 to 1.32 while these values in the roots were in the range of 0.16–1.46, 0.30 to 0.83, 0.23 to 0.69, 0.07 to 0.19 and 0.16 to 0.51, respectively. The average, minimum and maximum values are also given in Table 10; indicating the contamination of the mangroves. The values higher than 1.0 indicated high accumulations in the leaves and roots, which is an indication of acute contamination. It was observed that high accumulation in leaves was at sites 1 and 4

E i were lower than 40 while the values of RI were lower than 150; r indicating low contamination danger. Furthermore, these values were compared with the standard indexed values (Table 2) and it was observed that the metal ions threatened at all the fourteen studied sites but of low-risk magnitude. These results are in good agreement with the values of Igeo; confirming moderate metal ions pollution at all the sites. These results could be attributed to large input of wastes from human sources, which include non-treated waste discharges at these sites including refineries. The similar observations have also been reported by Badr et al. (2009); confirming pollution due to the Aramaco refinery. 7

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Fig. 5. The spatial distribution of heavy metals (μg g−1) in the sediments at Yanbu shorelines.

Fig. 6. The chromium concentrations (μg/g) distribution in the study area.

are given in Table 11. Generally, a value of TF greater than 1.0 indicates an acute accumulation. It was observed that TF values were higher than 1.0 at sites 1, 2 and 3 for all the metal ions; except Cr at site 1. These observations confirmed higher metal ion accumulation tendency of leaves than roots. It may be due to the fact that, in general, TF values higher than one indicated a high potential for plant bioaccumulation from the contaminated sites (Srivastava et al., 2006; Usman and Mohamed, 2009; Usman et al., 2012). During the study, it was observed that the industrial and human impacts may be reasonable for introducing the different types of pollutants; including heavy metals in the coastal area of Saudi Arabia. Such

while it was only site 1 in case of the leaves. A comparison of the values of BCF was carried out in the leave and roots. It was observed that, generally, higher values of BCF were with leaves. 3.4.2. Translocation factor (TF) Translocation is a plant capability to absorb and distribute metal ions across the body. It was estimated by calculating the translocation factor (TF). The values of TF were calculated as per equation (8) and are given in Table 11. The values of TF for Cr, Cu, Ni, Pb, and Zn ranged from 0.35 to 2.29, 0.49 to 2.39, 0.42 to 1.94, 0.34 to 4.05 and 0.59 to 2.70, respectively. The average, minimum and maximum values of TF 8

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Table 6 Averages of geo-accumulation (Igeo) of the studied heavy metals in all sites. Sampling Sites

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Table 8 Potential Ecological Risk Indices (E i ) and Potential Toxicity Response Indices r (RI) of heavy metals.

Averages of Igeo of the studied trace elements Cr

Cu

Ni

Pb

Zn

−3.18 0.00 −0.69 −1.94 −1.29 −2.11 −0.44 −2.56 −2.11 1.10 −1.52 0.95 −2.11 −0.81

−1.74 −0.84 −0.47 −0.67 −1.85 −1.97 −1.04 −1.97 −1.96 1.69 −1.27 0.87 −1.73 −1.06

0.49 1.29 0.71 −0.09 0.40 0.20 0.71 −0.14 −0.03 3.01 0.63 2.48 0.17 0.55

−1.22 0.39 0.41 0.61 −0.76 −1.15 0.24 −1.38 −0.02 1.89 −0.96 1.18 0.57 0.61

−1.15 0.25 −0.31 −0.39 −0.68 −1.48 −0.42 −1.55 −1.12 1.84 −0.92 1.46 −0.86 0.07

Sampling Sites

Cr 1 0.33 2 3.00 3 1.86 4 0.78 5 1.23 6 0.70 7 2.21 8 0.51 9 0.69 10 6.42 11 1.05 12 5.80 13 0.70 14 1.71 Descriptive statistics Average 1.93 Max. 6.42 Min. 0.33

Table 7 Contamination factor, pollution index and degree of pollution of heavy metals in studied sites. Sites

Contamination factor Cr

1 0.17 2 1.50 3 0.93 4 0.39 5 0.61 6 0.35 7 1.11 8 0.25 9 0.35 10 3.21 11 0.52 12 2.90 13 0.35 14 0.86 Descriptive statistics Min. 0.17 Max. 3.21 Average 0.96 Std. Deviation 0.962

PL Index

Cu

Ni

Pb

Zn

0.45 0.84 1.08 0.94 0.41 0.38 0.73 0.38 0.38 4.83 0.62 2.75 0.45 0.72

0.62 1.08 0.72 0.42 0.58 0.51 0.72 0.40 0.43 3.56 0.68 2.47 0.50 0.65

0.65 1.97 1.99 2.29 0.88 0.67 1.78 0.58 1.48 5.57 0.77 3.41 2.22 2.29

0.68 1.78 1.21 1.15 0.94 0.54 1.12 0.51 0.69 5.38 0.79 4.13 0.83 1.58

0.38 4.83 1.07 1.242

0.40 3.56 0.95 0.915

0.58 5.57 1.90 1.343

0.51 5.38 1.52 1.439

Potential ecological risk indices for individual metal (E i ) r

Potential toxicity response indices (RI)

Cu

Ni

Pb

Zn

2.25 4.19 5.41 4.71 2.07 1.92 3.66 1.91 1.92 24.13 3.11 13.74 2.26 3.59

3.73 6.49 4.32 2.49 3.48 3.05 4.34 2.40 2.60 21.34 4.08 14.80 2.97 3.88

3.23 9.84 9.96 11.45 4.42 3.37 8.88 2.89 7.41 27.83 3.85 17.03 11.11 11.44

0.68 1.78 1.21 1.15 0.94 0.54 1.12 0.51 0.69 5.38 0.79 4.13 0.83 1.58

10.21 25.30 22.76 20.57 12.14 9.57 20.21 8.22 13.31 85.10 12.89 55.50 17.87 22.19

5.35 24.13 1.91

5.71 21.34 2.40

9.48 27.83 2.89

1.52 5.38 0.51

23.99 85.10 8.22

sediments and mangroves samples. First, copper was compared at the Saudi coast Arabian gulf; Saudi coast (Sadiq and Zaidi, 1994), Farasan island; Al-Shouiba, Saudi Arabia (Usman et al., 2013; Abohassan, 2013) and Red Sea coastal areas, Saudi Arabia (Al-Zaharani et al., 2018). The concentrations of copper at all sites ranged from 1.8 to 112.0 μg g−1 while in this study it was 48.1 μg g−1. The concentration of copper was in the range of 4.4–270.6 μg g−1 in mangroves plants at different sites while it was from 23.1 to 29.0 μg g−1 in this work. The values of BCF and TF were in the range of 0.59–9.08 and 0.1 to 1.74 while these vlues in the present study ranged from 0.6 to 1.0 and 1.45. Similarly, the concentrations of lead metal ion in the sediment at different sites were in the range of 0.5–45.2 μg g−1 while it was 37.9 μg g−1 in the present study. The values in the mangroves at different sites ranged from 3.67 to 6.9 μg g−1 with 0.1–9.45 and 0.11 to 1.42 as BCF and TF while these values were 3.6–4.8 μg g−1, 0.13 to 0.27 and 1.73 at the reported sites. The zinc metal ion contamination was compared with only the Saudi coast site. The concentration in the sediment at this site (present study) and the reported sites were 7.3 and 144.7 μg g−1. The concentrations in the mangroves at the Saudi coast and this study were 11.0 and 43.3–59.8 μg g−1. The BCF ranged from 0.36 to 0.63 at the reported site while it was 1.56 at the Saudi coast site. Chromium metal ion contamination was compared at the Red Sea coastal areas, Saudi Arabia, Saudi coast, Farasan island and Al-Shouiba, Saudi Arabia sites with concentration range 8.8–46.11 μg g−1 while it was 86.7 μg g−1 in the reported study. The values in the mangroves ranged from 4.35 to 17.46 μg g−1 at these sites in comparison to 27.0–27.9 μg g−1 at the reported study. The values of BCF and TF were 0.23–4.23 and 0.25–0.95 μg g−1 at three sites while these were 0.25–0.90 and 1.22 μg g−1 at the reported sites. The last metal ion compared was nickel at the Saudi coast, Farasan island; Al-Shouiba, Saudi Arabia and Red Sea coastal areas, Saudi Arabia with sediment concentrations of 8.50–64.7 μg g−1 while it was 64.7 μg g−1 at the studied site. The values in the mangroves were in the range of 2.30–6.70 μg g−1 at these sites but these were 27.9–32.6 μg g−1 at the reported sites. The values of BCF and TF at these sites were 0.46–3.01 and 0.20–1.29 while these values were 0.48–0.68 and 1.25 μg g−1. All these values were analyzed critically and after comparison it was observed that the metal ions contamination at the reported site is comparable and moderate.

0.46 1.37 1.12 0.83 0.66 0.48 1.03 0.41 0.57 4.40 0.67 3.08 0.68 1.07

types of observations have already been put forwarded by the other authors in the literature (Alharbi et al., 2018b; Badr et al., 2009). Most probably, the industrial and mad made activities are responsible for heavy metal ions contamination at the reported sites. 3.5. Comparison of the metal ions concentrations with previously reported values The results obtained in this article are compared with the concentrations of the reported metal ions; previously described in other research papers. The comparisons were made with national and international studies. The comparisons were also made of the concentrations of the metal ions both in the sediments and mangroves plants. These national and international comparisons are given in Tables 12 and 13, respectively. The average values were taken for comparison purposes. These sorts of comparisons are very important to determine the degree of the contamination and understand the situation of Yanbu coast at national and international levels. 3.5.1. National comparison Heavy metals contamination levels at Yanbu coast, Red Sea were compared with other areas in the Kingdom of Saudi Arabia. The sites compared are in terms of the types of metal ions contamination in the

3.5.2. International comparison The metal ions contamination at the reported sites was also 9

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Table 9 Concentrations of heavy metals in leaves and roots of the mangrove Avecinia marina in mg/kg. Metal Concentration in roots

Metal Concentration in leaves

Sites

1 2 3 4 Average Min Max

Cr

Cu

Ni

Pb

Zn

Cr

Cu

Ni

Pb

Zn

21.8 16.3 40.5 29.4 27.0 16.3 40.5

16.8 17.2 37.3 21.2 23.1 16.8 37.3

29.0 17.2 38.2 27.4 27.9 17.2 38.2

2.5 2.4 6.7 3.0 3.6 2.4 6.7

31.5 38.1 62.4 41.2 43.3 31.5 62.4

50.1 23.7 14.2 23.5 27.9 14.2 50.1

40.2 19.2 18.1 38.6 29.0 18.1 40.2

56.3 25.3 16.1 32.2 32.5 16.1 56.3

9.9 3.0 2.3 3.9 4.8 2.3 9.9

84.9 45.1 36.8 72.6 59.8 36.8 84.9

while these were 3.6–4.8 μg g−1 in the present study. The values of BCF at these different sites and of the current study were 0.06–0.36 and 0.13 to 0.27. On the other hand, the values of TF at these different and current sites were 0.53–0.99 and 0.27 μg g−1, respectively. Also, the concentrations of zinc metal ion were compared with the studies of Australia, China, Hongkong, Malaysia, and Pakistan countries. The concentrations of zinc metal ions were in the range of 10.0–243.0 μg g−1 while it was 144.7 μg g−1 at the reported site. The zinc concentrations in mangroves at these different sites were in the order of 5.31–295.0 μg g−1 while these were 43.3–59.8 μg g−1 in the reported study. The values of BCE at the different countries’ locations and the reported study were 0.10–1.21 and 0.36–0.63 μg g−1. Contrarily, the values of TF at these different and current sites were 0.08–1.57 and 1.56, respectively. The metal ions chromium and nickel were compared with the South China sea site only. The concentration of chromium in sediment was 75.70 μg g−1 while it was 86.70 μg g−1 at the reported site. The values of BCF and TF in South China and the reported sites were 0.36 and 0.59 to 1.06; and 0.22 and 1.22. The sediment concentrations of nickel at South China sea and the present sites were 40.40 and 64.7 μg g−1. The values of BCF and TF at the South China and the reported sites were 0.27 and 0.48 to 0.68; and 0.38 and 1.25. The values of BCF and TF greater than 1.0 indicate acute contamination. It was observed that these values were almost greater than 1.0 in the present study for all the studied metal ions. Also, these values of BCF and TF were compared with the international studies and it was observed that most of the international studies have lower values of BCF and TF than 1.0. These facts clearly indicate that the site of Yanbu is more polluted than other sites reported in the different international studies. It may due to the heavy industrial activities at the Yanbu site. Finally, it was observed and recorded that Yanbu site is more contaminated than many of the international sites studied.

Table 10 Biological Concentration factors of heavy metals in mangrove, Avecinia marina, leaves and roots grown around Yanbu, KSA. Biological Concentration Factor (BCF) Sites Cr

1 2 3 4 Average Min Max

Cu

Ni

Pb

Zn

leaf

root

leaf

root

leaf

root

leaf

root

leaf

root

3.36 0.50 0.05 0.31 1.06 0.05 3.36

1.46 0.35 0.16 0.38 0.59 0.16 1.46

1.98 0.69 0.15 1.19 1.00 0.15 1.98

0.83 0.61 0.30 0.66 0.60 0.30 0.83

1.33 0.55 0.10 0.73 0.68 0.10 1.33

0.69 0.37 0.23 0.62 0.48 0.23 0.69

0.77 0.19 0.03 0.08 0.27 0.03 0.77

0.19 0.16 0.10 0.07 0.13 0.07 0.19

1.32 0.60 0.09 0.48 0.63 0.09 1.32

0.49 0.51 0.16 0.27 0.36 0.16 0.51

Table 11 Translocation factors of heavy metals in mangrove, Avecinia marina, leaves and roots grown around Yanbu, KSA. Translocation factor Sites

1 2 3 4 Average Min Max

Cr

Cu

Ni

Pb

Zn

2.29 1.45 0.35 0.80 1.22 0.35 2.29

2.39 1.12 0.49 1.82 1.45 0.49 2.39

1.94 1.47 0.42 1.18 1.25 0.42 1.94

4.05 1.24 0.34 1.28 1.73 0.34 4.05

2.70 1.18 0.59 1.76 1.56 0.59 2.70

compared with the international studies to evaluate the extent of the threat at Yanbu region, Red Sea Saudi coast. This comparison may be useful to analyze various factors responsible for the contamination at various sites. The results of the reported study were compared with different published papers at the diverse sites of five countries i.e. Australia (Saenger et al., 1990; MacFarlane, 2002; Alongi et al., 2003; MacFarlane et al., 2003), China (Peng et al., 1997); Lian et al., 1999); Li et al., 2016), Malaysia (ELTurk et al., 2018), Hong Kong (Chen et al., 2003) and Pakistan (Zahir et al., 2004). First of all, copper was compared with the studies of Australia, China, Hong Kong, Malaysia and Pakistan. The sediment concentrations at these sites range from 1.0 to 102 μg g−1 while it was 48.1 μg g−1 at the reported site. The values in the mangroves at the different sites were in the range of 2.69–101.0 μg g−1 while these were 23.1 and 29.0 μg g−1 at the reported site. The values of BCE at these sites were 0.10–1.66 μg g−1 while these were 0.6 and 1.0 μg g−1 at the reported sites. The values of TF at these different sites were in the range of 0.09–2.27 while it was 1.45 μg g−1 in the current study. Next is lead, which was compared with studies of Australia, China, Hongkong, Malaysia, and Pakistan. The sediment concentrations at the different sites were ranged from 8.0 to 76.63 μg g−1 while it was 37.9 μg g−1 at the reported site. The lead concentrations in the mangroves were ranged from 1.8 to 23.0 μg g−1

4. Conclusion The concentrations of the metal ions Cr, Cu, Ni, Pb, and Zn were in the ranges of 14.9–289, 17.2 to 217.2, 27.3 to 241.8, 11.5 to 111.3 and 48.8v 511.5 μg g−1. The spatial distributions of these metal ions showed significant inter-sites variability. The pollution status was classified as moderate to high. The localized accumulation of metals (Cr, Cu, Ni, Pb, and Zn) was observed at sites 10 and 12; due to the industrial activities and muddy and fine sedimentary nature. The ecological pollution indices (Igeo, CF, PLI, E i and RI) indicated site 10 as r moderately polluted with chromium, copper, lead and zinc; and heavily contaminated by nickel-metal ion. Besides, Igeo for nickel was high at point 2 (1.29) and 12 (2.48); indicating moderate contamination. Additionally, the lead and zinc metal ions had 1.18 and 1.46 as Igeo values at site 12; indicating moderate pollution. The values of the contamination factor (CF) of the reported metal ions (Cr, Cu, Ni, Pb, and Zn) were in the range of 0.17–1.50, 0.38 to 4.83, 0.40 to 3.56, 0.58 to 2.29 and 0.51 to 3.38; indicating moderate pollution. The pollution load index (PLI) values ranged from 0.41 to 4.40. The obtained high PLI value might be directly associated with high concentrations of heavy 10

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Table 12 National comparison of distribution of the heavy metals: (a) copper, (b) lead, (c) zinc, (d) chromium and (e) nichel in sediment (μg g-1) and in the roots and leaves (μg g−1) of mangrove species, Avicennia marina. Locations Copper (Cu) Yanbu Saudi coast, Arabian Gulf Saudi coast, Farasan island Al-Shouiba, Saudi Arabia Red Sea coastal areas, Saudi Lead (Pb) Yanbu Saudi coast, Arabian Gulf Saudi coast, Farasan island Al-Shouiba, Saudi Arabia Red Sea coastal areas, Saudi Zinc (Zn) Yanbu Saudi coast Chromium (Cr) Yanbu Red Sea coastal areas, Saudi Saudi coast, Farasan island Al-Shouiba, Saudi Arabia Ni Yanbu Saudi coast, Farasan island Al-Shouiba, Saudi Arabia Red Sea coastal areas, Saudi

Sediment

Roots

Leaves

Root BCF

Leaf BCF

TF

Refs.

23.1

29.0 4.4 356.6 6.38 13.24

0.6

1.0 2.42 1.25 1.0 0.88

1.45

Arabia

48.1 1.8 112.0 4.10 22.87

Current study Sadiq and Zaidi (1994) Usman et al. (2013) Abohassan (2013) Al-Zaharani et al. (2018)

3.6

4.8 6.9 – – 3.79

0.13

0.27 0.58 – 1.07 1.57

1.73

Arabia

37.9 12 45.2 0.5 3.82

Arabia

270.6 6.79 9.82

– – 3.67

9.08 9.23 0.59

0.01 9.45 1.60

1.30 0.10 1.74

0.00 0.11 1.42

Current study Sadiq and Zaidi (1994) Usman et al. (2013) Abohassan (2013) Al-Zaharani et al. (2018)

144.7 7.3

43.3

59.8 11

0.36

0.63 1.56

1.56

Current study Sadiq and Zaidi (1994)

86.7 46.11 9.6 8.8

27.0 17.46 14.9 4.53

27.9 14.96 9.30 4.35

0.59 0.47 4.23 3.43

1.06 0.43 0.23 0.34

1.22 0.90 0.42 0.25

Current study Al-Zaharani et al. (2018) Usman et al. (2013) Abohassan (2013)

64.7 8.50 27.40

27.9 4.02 4.07

32.6 2.30 6.70

0.48 3.01 1.21 0.49

0.68 1.06 0.46 0.47

1.25 0.35 0.20 1.29

Current study Usman et al. (2013) Abohassan (2013) Al-Zaharani et al. (2018)

Arabia

Table 13 International comparison of distribution of the heavy metals: (a) copper, (b) lead, (c) zinc, (d) chromium and (e) nichel (μg g-1) in sediment and in the roots and leaves of mangrove species, Avicennia marina. Location Copper (Cu) Yanbu W. Australia SE Australia SE Australia N. Australia Shenzhen, China Hainan Island, China South China Sea Ting Kok, Hong Kong Malaysia Karachi, Pakistan Lead (Pb) N. Australia Shenzhen, S. China Ting Kok, Hong Kong Shenzhen, China Karachi, Pakistan South China Sea Malaysia

Sediment

Roots

Leaves

Root BCF

Leaf BCF

TF

References

48.1 16 61 1–102 25–134 36

23.1 18 101

29.0 7.3 9 3.0–20 9.8–26 5.2 6.3 5.20 16 3.65 3.2–14

0.6 1.16 1.66

1.0 0.47 0.15 0.25 0.28 0.15 0.46 – 1.26 – 0.27

1.45 0.40 0.09

Current study Alongi et al. (2003) MacFarlane et al. (2003) MacFarlane (2002) Saenger et al. (1990) Peng et al. (1997) Lian et al. (1999) Li et al. (2016) Chen et al. (2003) ELTurk et al. (2018) Zahir et al. (2004)

13

79.72 13 3.55 12–56

13.0 13 2.69

37.9 8.0–27 31 33 34 32–59 – 76.63

3.6 3.4 15 3.5 3.50 14.32

0.37 0.10 1.02 0.78

4.8 1.1–4.7 1.8 8.0 1.9 11–23 1.90 11.89

0.13 0.11 0.44 0.10 – 0.20

0.27 0.15 0.06 0.24 0.06 0.36 – –

0.39 2.27 1.23 1.47

1.73 0.53 0.55 0.54 – 0.99

Current study Saenger et al. (1990) Zheng and Lin (1996) Chen et al. (2003) Tam et al. (1995) Zahir et al. (2004) Li et al. (2016) ELTurk et al. (2018)

Zinc (Zn) 144.7 34 55 99 106 46–210 243 10–289 35–67

43.3 16 16 70 79

28.84

5.78

59.8 14 15 23 23 16–52 25 12–46 10–19 20 5.31

86.7 75.70

27.0

South China Sea

64.7 40.40

27.9

South China Sea

W. Australia Ting Kok, Hong Kong Shenzhen, S. China Shenzhen, China N. Australia SE Australia SE Australia Karachi, Pakistan Hainan Island, China Malaysia Chromium (Cr)

295

0.36 0.48 0.29 0.71 0.75

1.56 0.88 0.96 0.33 0.29

0.19

0.63 0.42 0.28 0.23 0.22 0.31 0.10 0.35 0.29 0.70 –

1.57

Current study Alongi et al. (2003) Chen et al. (2003) Zheng and Lin (1996) Peng et al. (1997) Saenger et al. (1990) MacFarlane et al. (2003) MacFarlane (2002) Zahir et al. (2004) Lian et al. (1999) ELTurk et al. (2018)

27.9

0.59 0.36

1.06 –

1.22 0.22

Current study Li et al. (2016)

32.6

0.48 0.27

0.68 –

1.25 0.38

Current study Li et al. (2016)

1.21

0.08

Ni

11

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metals in the sites; particularly at the sites 10 (PLI = 4.40) and 12 (PLI = 3.08). The values of ecological risk coefficients (E i ) for Cr, Cu, r Ni, Pb, and zinc were in the ranges of 0.33–6.42, 1.91 to 24.13, 2.40 to 21.34, 2.89 to 27.83, and 0.51 to 5.38. The values of potential toxicity response indices (RI) were in the ranges of 8.22–85.10. It was concluded that the values of E i were lower than 40 while the values of RI r were lower than 150; indicating low-risk contamination. The values of metal ions in the roots of the mangroves were 16.3–40.5, 16.8 to 37.3, 17.2 to 38.2, 2.4 to 6.7 and 31.5–62.4 mg/kg for Cr, Cu, Ni, Pb, and Zn metal ions while these values in the roots were 14.2–50.1, 18.1 to 40.2, 16.1 to 56.3, 2.3 to 9.9 and 36.8–84.9 μg g−1. These values indicated quite high metal ions contamination in the mangroves. The values of BCF for Cr, Cu, Ni, Pb and Zn in the leaves were in range of 0.05–3.36, 0.15 to 1.98, 0.10 to 1.33, 0.03 to 0.77 and 0.09 to 1.32 while these values in the roots were in the range of 0.16–1.46, 0.30 to 0.83, 0.23 to 0.69, 0.07 to 0.19 and 0.16 to 0.51. The values higher than 1.0 indicated high accumulations in the leaves and roots, which is an indication of acute contamination. The values of TF for Cr, Cu, Ni, Pb, and Zn ranged from 0.35 to 2.29, 0.49 to 2.39, 0.42 to 1.94, 0.34 to 4.05 and 0.59 to 2.70. Generally, a value of TF greater than 1.0 indicated acute accumulation. It was observed that TF values were higher than 1.0 at sites 1, 2 and 3 for all the metal ions; except Cr at site 1. Briefly, the Red Sea coast at Yanbu, Saudi Arabia is moderate to heavily contaminated, which may affect the aquatic lives and human beings.

Badr, N.B., El-Fiky, A.A., Mostafa, A.R., Al-Mur, B.A., 2009. Metal pollution records in core sediments of some Red Sea coastal areas, Kingdom of Saudi Arabia. Environ. Monit. Assess. 155, 509–526. Balakrishnan, P., Saleem, A., Mallikarjun, N.D., 2011. Groundwater quality mapping using geographic information system (GIS): a case study of Gulbarga City, Karnataka, India. Afr. J. Environ. Sci. Technol. 5, 1069–1084. Banerjee, S., Kumar, A., Maiti, S.K., Chowdhury, A., 2016. Seasonal variation in heavy metal contaminations in water and sediments of Jamshedpur stretch of Subarnarekha river, India. Environ. Earth. Sci. 75, 265. Bastakoti, U., Robertson, J., Alfaro, A.C., 2018. Spatial variation of heavy metals in sediments within a temperate mangrove ecosystem in northern New Zealand. Mar. Pollut. Bull. 135, 790–800. Birch, G., 2017. Assessment of human-induced change and biological risk posed by contaminants in estuarine/harbour sediments: sydney Harbour/estuary (Australia). Mar. Pollut. Bull. 116, 234–348. Bodin, N., N′Gom-Kâ, R., Kâ, S., Thiaw, O.T., TitodeMorais, L., LeLoc′h, F., RozuelChartier, E., Auger, D., Chiffoleau, J.-F., 2013. Assessment of trace metal contamination in mangrove ecosystems from Senegal West Africa. Chemosphere 90, 150–157. Bouillon, S., 2011. Storage beneath mangroves. Nat. Geosci. 4, 282–283. Camp, V.E., Roobol, M.J., 1991. Geologic Map of Cenozoic Lava Field of Harrat Rahat, Kingdom of Saudi Arabia, vol. 123 Directorate General of Mineral Resources, Geosciences Map GM- Scale 250,000 with text. Chai, M., Li, R., Ding, H., Zan, Q., 2019. Occurrence and contamination of heavy metals in urban mangroves: a case study in Shenzhen, China. Chemosphere 219, 165–173. Chakraborty, S., Chakraborty, P., Nath, B.N., 2015. Lead distribution in coastal and estuarine sediments around India. Mar. Pollut. Bull. 97 (1–2), 36–46. Chen, X.Y., Tsang, E.P.K., Chan, A.L.W., 2003. Heavy metal contents in sediments, mangroves and bivalves from Ting Kok, Hong Kong. China Environ. Sci 23, 480–484. El-Sorogy, A., Al-Kahtany, K., Youssef, M., Al-Kahtany, F., 2018. Distribution and metal contamination in the coastal sediments of Dammam Al-Jubail area, Arabian Gulf, Saudi Arabia. Mar. Pollut. Bull. 128, 8–16. ELTurk, M., Abdullah, R., Rozainah, M.Z., Bakar, N.K.A., 2018. Evaluation of heavy metals and environmental risk assessment in the Mangrove Forest of Kuala Selangor estuary, Malaysia. Mar. Pollut. Bull. 136, 1–9. Eong, O.J., 1995. The ecology of mangrove conservation and management. Hydrobiologia 295, 343–351. Feng, J., Zhu, X., Wu, H., Ning, C., Lin, G., 2017. Distribution and ecological risk assessment of heavy metals in surface sediments of a typical restored mangrove–aquaculture wetland in Shenzhen, China. Mar. Pollut. Bull. 124, 1033–1039. Fernandes, L., Nayak, G.N., Ilangovan, D., 2012. Geochemical assessment of metal concentrations in mangrove sediments along Mumbai Coast, India World Academy of Science. Eng. Technol. 61, 258–263. Fernández-Cadena, J.C., Andrade, S., Silva-Coello, C.L., De la Iglesia, R., 2014. Heavy metal concentration in mangrove surface sediments from the north-west coast of South America. Mar. Polln. Bull. 82, 221–226. Gu, Y.G., Huang, H.H., Liu, Y., Gong, X.Y., Liao, X.L., 2018. Non-metric multidimensional scaling and human risks of heavy metal concentrations in wild marine organisms from the Maowei Sea, the Beibu Gulf, South China Sea. Environ. Toxicol. Pharmacol. 59, 119–124. Hakanson, L., 1980. An ecological risk index for aquatic pollution control. A sedimentological approach. J. Water Resour. 14, 975–1001. Hao, Z., Chen, L., Wang, C., Zou, X., Zheng, F., Feng, W.,, et al., 2019. Heavy metal distribution and bioaccumulation ability in marine organisms from coastal regions of Hainan and Zhoushan, China. Chemosphere 226, 340–350. Harty, C., 1997. Mangroves in New South Wales and Victoria. Vista Publications, Melbourne, pp. 47pp. Igboekwe, M.U., Akankpo, A.O., 2011. Application of geographic information system (GIS) in mapping groundwater quality in uyo, Nigeria. Int. J. Geosci. 2, 394–397. Jahan, S., Strezov, V., 2018. Comparison of pollution indices for the assessment of heavy metals in the sediments of seaports of NSW, Australia. Mar. Pollut. Bull. 128, 295–306. Johnson, P.R., 1998. Tectonic Map of Saudi Arabia and Adjacent Areas. Ministry of Petroleum and Mineral Resources, Deputy Ministry for Mineral Resource Technical Report USGS-TR-98-3 (IR 948). Krishnaraj, S., Kumar, S., Elango, K.P., 2015. Spatial analysis of groundwater quality using geographic information system – a case study. IOSR J. Environ. Sci. Toxicol. Food Technol. 9, 1–6. Kulkarni, R., Deobagkar, D., Zinjarde, S., 2018. Metals in mangrove ecosystems and associated biota: a global perspective. Ecotoxicol. Environ. Saf. 153, 215–228. Li, R., Chai, M., Qiu, G.Y., 2016. Distribution, fraction, and ecological assessment of heavy metals in sediment-plant system in mangrove forest, South China Sea. PloS one 11 (1) p.e0147308. Lian, Y., Xu, J., Lin, P., Meguro, S., Kawachi, S., 1999. Five heavy metals in propagules of ten mangrove species of China. Journal of wood science 45 (4), 343–347. MacFarlane, G.R., Koller, C.E., Blomberg, S.P., 2007. Accumulation and partitioning of heavy metals in mangroves: a synthesis of field-based studies. Chemosphere 69, 1454–1464. MacFarlane, G.R., 2002. Leaf biochemical parameters in Avicennia marina (Forsk.) Vierh as potential biomarkers of heavy metal stress in estuarine ecosystems. Mar. Pollut. Bull. 44, 244–256. MacFarlane, G.R., Pulkownik, A., Burchett, M.D., 2003. Accumulation and distribution of heavy metals in the grey mangrove, Avicennia marina (Forsk.) Vierh.: biological indication potential. Environmental Pollution 123 (1), 139–151. Mackay, D., Fraser, A., 2000. Bioaccumulation of persistent organic chemicals: mechanisms and models. Environ. Pollut. 110, 375–391.

Acknowledgement The authors extend their sincere appreciation to the College of Science and Applied Science Research Center at Taibah University, Madinah, Saudi Arabia for support and funding for Group No #50267. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.marpolbul.2019.110669. References Abohassan, R.A., 2013. Heavy metal pollution in Avicennia marina mangrove systems on the Red Sea coast of Saudi Arabia. JKAU: Meteorol. Environ. Arid Land Agric. Sci. 24, 35–53. Al-Dabbagh, M.E., 2014. The Arabian Plate: unique fit of the earth's surface jig saw puzzle. Arab J Geosci 7, 3297–3307. Alharbi, O.M.L., Basheer, A.A., Khattab, R.A., Ali, I., 2018a. Health and environmental effects of persistent organic pollutants. J. Mol. Liq. 263, 442–453. Alharbi, O.M.L., Khattab, R.A., Ali, I., Binnaser, Y.S., Aqeel, A., 2018b. Evaluation of the heavy metals threat to the Yanbu shoreline, red sea, Saudi arabia. Mar. Freshw. Res. 69, 1557–1568. Ali, I., Alothman, Z.A., Alwarthan, A., 2017. Supra molecular mechanism of the removal of 17-β-estradiol endocrine disturbing pollutant from water on functionalized iron nano particles. J. Mol. Liq. 441, 123–129. Ali, I., Gupta, V.K., Aboul-Enein, H.Y., 2005. Metal ion speciation and capillary electrophoresis: application in the new millennium. Electrophoresis 26, 3988–4002. Ali, I., Wani, W.A., Saleem, K., Hseih, M.F., 2013. Design and synthesis of thalidomide based dithiocarbamate Cu(II), Ni(II) and Ru(III) complexes as anticancer agents. Polyhedron 56, 134–143. Alongi, D.M., 2002. Present state and future of the world's mangrove forests. Environ. Conserv. 29, 331–349. Alongi, D.M., Clough, B.F., Dixon, P., Tirendi, F., 2003. Nutrient partitioning and storage in arid-zone forests of the mangroves Rhizophora stylosa and Avicenniamarina. Trees 17 (1), 51–60. Al-Zaharani, D.A., Selim, E.M.M., El-Sherbiny, M.M., 2018. Ecological assessment of heavy metals in the grey mangrove (Avicennia marina) and associated sediments along the red sea coast of Saudi Arabia. Oceanologia 60, 513–526. Analuddin, K., Sharma, S., Septiana, A., Sahidin, I., Rianse, U., Nadaoka, K., 2017. Heavy metal bioaccumulation in mangrove ecosystem at the coral triangle ecoregion, Southeast Sulawesi, Indonesia. Mar. Pollut. Bull. 125, 472–480. Aqeel, A., Al-Amry, A., Alharbi, O., 2017. Assessment and geospatial distribution mapping of fluoride concentrations in the groundwater of Al-Howban Basin, Taiz-Yemen. Arabian. J. Geosci. 10, 312. Aqeel, A.M., 2016. Investigation of expansive soils in obhor sabkha, jeddah-Saudi arabia. Arab J Geosci 9, 314. https://doi.org/10.1007/s12517-016-2341-x.

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Taylor, S.R., McLennan, S.M., 1995. The geochemical evolution of the continental crust. Rev. Geophys. 33, 241–265. Thanh-Nho, N., Marchand, C., Strady, E., Vinh, T.V., Nhu-Trang, T.T., 2019. Metals geochemistry and ecological risk assessment in a tropical mangrove (Can Gio, Vietnam). Chemosphere 219, 365–382. Tomlinson, D.L., Wilson, J.G., Harris, C.R., Jeffney, D.W., 1980. Problems in the assessment of heavy metal levels in estuaries and the formation of pollution index. Helgol. Wiss. Meeresunters. 33, 566–572. Turekian, K.K., Wedepohl, K.H., 1961. Distribution of the elements in some major units of the earth's crust. Geol. Soc. Am. Bull. 72, 175–192. Usman, A.R.A., Mohamed, H.M., 2009. Effect of microbial inoculation and EDTA on the uptake and translocation of heavy metal by corn and sunflower. Chemosphere 76, 893–899. Usman, A.R., Alkredaa, R.S., Al-Wabel, M.I., 2013. Heavy metal contamination in sediments and mangroves from the coast of Red Sea: Avicennia marina as potential metal bioaccumulator. Ecotoxicol. Environ. Saf. 97, 263–270. Usman, A.R., Lee, S.S., Awad, Y.M., Lim, K.J., Yang, J.E., Ok, Y.S., 2012. Soil pollution assessment and identification of hyperaccumulating plants in chromated copper arsenate (CCA) contaminated sites, Korea. Chemosphere 87, 872–878. Wang, C., Zou, X., Feng, Z., Hao, Z., Gao, J., 2018. Distribution and transport of heavy metals in estuarine-inner shelf regions of the East China Sea. Sci. Total Environ. 644, 298e305. West, R.J., Thorogood, C.A., Williams, R.J., 1983. Environmental stress causing mangrove ’dieback’ in NSW. Aust. Fish 16–20. Yoon, J., Cao, X., Zhou, Q., Ma, L.Q., 2006. Accumulation of Pb, Cu, and Zn in native plants growing on a contaminated Florida site. Sci. Total Environ. 368, 456–464. Zahir, E., Naqvi, I.I., Zehra, I., 2004. Spatial and temporal variation of heavy metals in mangrove and sediment along Karachi coastal areas, Pakistan. J. Saudi Chem. Soc. 8, 197–202. Zheng, W., Lin, P., 1996. Accumulation and distribution of Cu, Pb, Zn and Cd in Avicennia marina mangrove community of Futian in Shenzhen. Oceanologia et Limnologia Sinica 27 (4), 388–393.

Mandura, A.S., Khafaji, A.K., Saifullah, S.M., 1988. Ecology of a mangrove stand of a central red sea coast area: ras hatiba (SaudiArabia). Proc. Saudi Biol.Soc. 11, 85–112. Mandura, A.S., Saifullah, S.M., Khafaji, A.K., 1987. Mangrove ecosystem of southern red sea coast of Saudi arabia. Proc. Saudi Biol. Soc. 10, 165–193. Mashiatullah, A., Chaudhary, M.Z., Ahmad, N., Ahmad, N., Javed, T., Ghaffar, A., 2015. Geochemical assessment of metal pollution and ecotoxicology in sediment cores along Karachi Coast, Pakistan. Environ. Monit. Assess. 187, 249. Müller, G., 1979. Heavy metals in the sediment of the Rhine-Changesseity. Umsch. Wiss. Tech. 79, 778–783. Müller, G., 1981. Die Schwermetallbelastung der Sedimenten des Neckars und Seiner Nebenflüsse. Chem. Ztg. 6, 157–164. Peng, L., Zheng, W., Li, Z., 1997. Distribution and accumulation of heavy metals in Avicennia marina community in Shenzhen, China. J. Environ. Sci.. (China) 9, 427–429. Peters, E.C., Gassman, N.J., Firman, J.C., Richmond, R.H., Power, E.A., 1997. Ecotoxicology of tropical marine ecosystems. Environ. Toxicol. Chem. 16, 12–40. Praveena, S.M., Radojevic, M., Abdullah, M.H., Aris, A.Z., 2008. Application of sediment quality guidelines in the assessment of mangrove surface sediment in Mengkabong lagoon, Sabah, Malaysia. J. Environ. Health. Sci. Eng. 5, 35–42. Sadiq, M., Zaidi, T.H., 1994. Sediment composition and metal concentrations in mangrove leaves from the Saudi coast of the Arabian Gulf. Sci. Total Environ. 155, 1–8. Saenger, P., McConchie, D., Clark, M.W., 1990. Mangrove forests as a buffer zone between anthropologically polluted areas and the sea. In: Saenger, P. (Ed.), Proceedings of the 1990 CZM Workshop, Yeppoon, Qld, pp. 280–297. Singh, C.K., Shashtri, S., Mukherjee, S., 2011. Integrating multivariate statistical analysis with GIS for geochemical assessment of groundwater quality in Shiwaliks of Punjab, India. Environmental Earth Sciences 62, 1387–1405. Srivastava, M., Ma, L.Q., Santos, J.A.G., 2006. Three new arsenic hyperaccumulating ferns. Sci. Total Environ. 364, 24–31. Tam, N.F.Y., Li, S.H., Lan, C.Y., Chen, G.Z., Li, M.S., Wong, Y.S., 1995. Nutrients and heavy metal contamination of plants and sediments in Futian mangrove forest. Hydrobiologia 295, 149–158.

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